Coregulation of Transcription Factors and microRNAs in
Human Transcriptional Regulatory Network
Cho-Yi Chen1,2,3, Shui-Tein Chen2, Chiou-Shann Fuh4, Hsueh-Fen Juan3§, Hsuan-Cheng
Huang1§
1
Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National
Yang-Ming University, Taipei, Taiwan
2
Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
3
Genome and Systems Biology Degree Program, Department of Life Science, Institute of
Molecular and Cellular Biology, Graduate Institute of Biomedical Electronics and
Bioinformatics, National Taiwan University, Taipei, Taiwan
4
Department of Computer Science and Information Engineering, National Taiwan University,
Taipei, Taiwan.
§
Corresponding authors
Email addresses:
CYC: joeychen@gate.sinica.edu.tw
STC: bcchen@gate.sinica.edu.tw
CSF: fuh@csie.ntu.edu.tw
HFJ: yukijuan@ntu.edu.tw
HCH: hsuancheng@ym.edu.tw
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Abstract
Background:
MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression at the posttranscriptional level. Recent studies have suggested that miRNAs and transcription factors are
primary metazoan gene regulators; however, the crosstalk between them still remains unclear.
Methods:
We proposed a novel model utilizing functional annotation information to identify significant
coregulation between transcriptional and post-transcriptional layers. Based on this model,
function-enriched coregulation relationships were discovered and combined into different
kinds of functional coregulation networks.
Results:
We found that miRNAs may engage in a wider diversity of biological processes by
coordinating with transcription factors, and this kind of cross-layer coregulation may have
higher specificity than intra-layer coregulation. In addition, the coregulation networks reveal
several types of network motifs, including feed-forward loops and massive upstream
crosstalk. Finally, the expression patterns of these coregulation pairs in normal and tumour
tissues were analyzed. Different coregulation types show unique expression correlation
trends. More importantly, the disruption of coregulation may be associated with cancers.
Conclusion:
Our findings elucidate the combinatorial and cooperative properties of transcription factors
and miRNAs regulation, and we proposes that the coordinated regulation may play an
important role in many biological processes.
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Background
Transcriptional regulatory networks describe the interactions between transcriptional
regulatory proteins and their target genes [1-3]. These regulators, known as transcription
factors (TFs), are proteins that bind to specific DNA sequences and thereby control the
transcription of genetic information encoded in DNA sequences. The interactions between
TFs and target genes regulate the transcriptional activities of genome and thus determine the
global gene expression program of a living cell.
In the last decade, microRNAs (miRNAs) have emerged as another prominent class of
gene regulators. miRNAs are endogenous small RNA molecules that are abundant in animals,
plants, and some viruses. They can reduce stability and/or translation activity of fully or
partially sequence-complementary messenger RNAs (mRNAs), thus regulating gene
expression at the post-transcriptional level. It has been found that miRNAs may control many
biological processes in development, differentiation, growth, and even cancer development
and progression [4-6].
Recent studies have suggested that miRNAs and TFs are primary metazoan gene
regulators, and they seem to function in a similar regulatory logic, such as pleiotropy,
combinatorial and cooperative activity, regulation, and even network motifs [7-8]. However,
how miRNAs interplay and coordinate with TFs in the regulatory network still remains
unclear. Since combinatorial interactions between miRNAs and TFs are complicated and thus
hard to be validated by high-throughput experiments, computational modelling may provide a
better clue to understand such complex relationships.
Currently, to uncover the coregulation interactions between miRNAs and TFs,
researchers have to overcome two challenges. One is the incomplete knowledge of regulatory
targets. Because the available experimentally verified targets of miRNAs and TFs are far
from complete, the regulatory target datasets for global analysis were mainly from
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computational prediction. The other challenge is about how to integrate transcriptional and
post-transcriptional layers to discover highly confident coregulation relationships. To solve
these problems, previous studies have developed a bottom-up strategy; that is, they inferred
the coordination between two upstream regulators from their downstream shared targets [910]. These inferences were basically based on different probabilistic models and statistical
tests to measure the significance of shared targets between regulators. Indeed, the methods
successfully eliminated those insignificant coregulation interactions occurred merely by
chance; however, the biological meanings were ignored in the integration of transcriptional
and post-transcriptional regulation interactions.
Here we proposed a novel framework utilizing functional annotation information to
identify significant coregulation between transcriptional and post-transcriptional layers.
Based on this model, function-enriched coregulation pairs were discovered, and the
regulators were subsequently linked by enriched functions. With these functional linkages,
we further constructed functional coregulation networks between regulators and investigated
their characteristics. Next, we searched for the network motifs consisting of those functionenriched coregulation pairs, and found that an abundance of pairs were closely connected in
their upstream. Finally, the expression patterns of function-enriched coregulation pairs were
analyzed. Different coregulation types showed distinct expression correlation trends. More
importantly, we found that the disruption of coregulation may be closely related to cancers.
Methods
Regulation relationships
The transcriptional regulation relationships between human transcription factors and their
target genes were collected from TRED [11]. The database provides genome wide promoter
annotation and transcription factor binding information from computational prediction and
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experimental evidence. A total of 133 TFs and 6,764 regulation relationships were collected
and constituted the human transcriptional regulation network for our analysis.
Since the available experimentally verified human miRNA targets are far from
complete and thus not enough for global analysis, we used predicted miRNA targets from the
TargetScan database (release 4.2) to perform the analysis [12]. In addition, different mature
miRNAs may have identical seed regions and thereby target the same binding sites. To
eliminate those coregulation interactions among the miRNAs with identical seed regions, we
grouped mature miRNAs into families based on the miRNA family information from
TargetScan. A total of 162 miRNA families and 7,521 target genes with 44,782 interactions
were collected.
It is still difficult to predicted the promoter region of miRNA genes in the genome.
But it has been known that embedded miRNAs frequently coexpress with their host genes
[13-14]. Therefore, we extracted miRNA host gene information from miRBase [15] and
integrated the embedded miRNAs biogenesis information into the established transcriptional
regulation network. A total of 310 premature miRNAs were found embedded in 259 host
genes. Most of them (93%) were resided in introns.
Identification of significant coregulation relationships
Combing all the potential targets of miRNAs and TFs, we firstly constructed two adjacency
matrixes describing the regulator-target interaction for TFs and miRNAs, respectively. Then
the two matrixes were combined into three cross-adjacency matrixes representing the shared
targets of TF-TF, miRNA-miRNA, and TF-miRNA coregulation pairs. An example of
identification of TF-miRNA coregulation pairs is shown in Figure 1.
Secondly, for each group of shared targets, the distribution of Gene Ontology (GO)
annotations [16] at the second level in the biological process namespace was calculated. We
chose the second level ontology because most of the genes were generally well-annotated at
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this level and these annotations provided a good balance between the sensitivity and the
specificity in the following functional enrichment test. The distributions were considered as
the functional profiles or fingerprints for these coregulation pairs.
Next, we utilized a randomization method to perform a permutation test for functional
enrichment. For each group of shared targets, we randomly selected a null group of the same
size from whole human genome as background. After 10,000 iterations, the log-likelihood
score under multivariate hypergeometric distribution was measured to quantify the
significance of functional enrichment. The correction for multiple comparisons was made
under 0.05 false discovery rate (FDR) [17]. The final results of significant coregulation pairs
were listed in additional file 1.
Functional linkages and networks
For each function-enriched coregulation pair, Fisher’s Exact Test following FDR correction
were conducted to identify enriched GO terms. Similarly, we only focused on the second
level terms in biological process namespace. A functional linkage was established if a GO
term overrepresented in the shared targets of a coregulation pair, implying that the two paired
regulators may function coordinately in the specific biological process. Based on these
linkages, we further constructed the functional coregulation networks (Figure 2). In order to
investigate the specificity of coregulation relationships and provide a global view, only those
linkages with relatively high significance that passed FDR-BL correction [17] were used to
construct the networks.
Enriched network motifs
We searched for network motifs preferentially occurred in function-enriched coregulation
pairs rather than random pairs by a resampling process. The predicted TF-targeting
interactions for miRNA genes were collected from miRBase [15] and from literature [9]. In
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addition, we assumed that those embedded miRNA genes have same transcription units with
their host genes and would be regulated together.
A total of 10,000 background sets of regulator pairs that have the same size as the set
of function-enriched pairs were randomly selected from the global network. For each type of
network patterns (sub-graphs), the observed frequency from the function-enriched
coregulation pairs was first calculated and compared to the background distribution for
assessment of significance. Only those network patterns with occurrence probabilities less
than 0.001 were considered significant motifs (see additional file 2 for these significant
motifs).
Analysis of expression profiles
The miRNA and mRNA expression profiles were adopted from a previous study [18]. A
total of 217 miRNAs and ~16,000 mRNAs across 8 human tissues (colon, pancreas, kidney,
bladder, prostate, uterus, lung, and breast) were measured using miRNA bead-arrays and
mRNA microarrays. Both normal and tumor samples were profiled for each tissue. For each
type of coregulation, we first generated background distribution by calculating the Pearson's
correlation coefficients (PCCs) of expression profiles between the two paired regulators in all
possible pairs (i.e., those pairs shared no targets and/or those pairs not identified as functionenriched). After that, the distribution of enriched coregulation pairs was calculated and shown
against the background.
Results
Functional coregulation pairs
After the integration of miRNA regulation into human transcriptional regulation network, we
adopted a novel strategy utilizing functional information to identify function-enriched
coregulation pairs, and establish function linkages for each pair. Traditional analysis of
functional enrichment was aimed at elucidating the regulatory roles of each individual
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regulator only, inevitably leaving some significant coregulation hidden in the traditional
views. Instead, based on our model, different regulation types involving single regulators or
combinations of regulators can all be studied and compared.
The distributions of different regulation types were grouped into two diagrams for
comparing. Figure 3A shows distributions of individual TF regulation and TF-TF
coregulation. The two distributions look similar; however, two biological processes,
pigmentation and reproductive process, emerge when it comes to TF-miRNA coregulation,
implying that the two biological processes may be the typical processes in which TFs should
coordinate with miRNAs to control the expression programs.
In contrast, miRNA-involving regulation shows divergent distributions in Figure 3B.
The top ranked biological processes of individual miRNA regulation were biological
regulation, cellular process, and developmental process, which were the previously known
miRNA-involving processes. On the other hand, biological adhesion was relatively high in
miRNA-miRNA coregulation, suggesting that miRNAs may regulate this process majorly in
a coordination manner.
Moreover, many biological processes enriched in TF-miRNA coregulation were
relatively poor in the regulation involving miRNAs only. In other words, those processes may
be the typical processes needed to be coordinately regulated by TFs and miRNAs, and the
coordination may provide a mechanism to switch expression programs. More importantly, it
suggested that, by coordinating with TFs, miRNAs may engage in a wider diversity of
biological processes, and these undiscovered processes were failed to be identified by
traditional analysis of functional enrichment for a single regulator.
Functional coregulation networks
In the previous section, different regulators were connected by identified functional linkages,
which represented that the two paired regulators may function in coordination with each other
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in a specific biological process. We further built up functional coregulation networks from
these linkages and found interesting properties in the networks.
Figure 4 shows the three subnetwork examples (the largest connected component) for
different coregulation types. Obviously, it appears that the TF-TF coregulation network
(Figure 4A) was in high density coregulation (avg. 3.52 links per pair) and full of diversity in
edge types (avg. 5.70 edge types per regulator); in other words, TFs may function together in
many kinds of biological processes. Likewise, the miRNA-miRNA coregulation network
(Figure 4B) showed similar high density coregulation (avg. 3.12 links per pair) but the
diversity of involving processes was lower (avg. 3.92 edge types per regulator) than TF-TF
coregulation. In contrast, the TF-miRNA coregulation network (Figure 4C) was in higher
specificity (avg. 1.48 links per pair; avg. 2.22 edge types per regulator); that is, a TF may
coordinate with different neighbour miRNAs in specific biological processes, and vice versa.
These results suggested that the cross-layer coregulation may have higher specificity than
intra-layer coregulation.
Network motifs for coregulation pairs
Many studies have been devoted to understanding network structures in gene regulatory
networks, and have found that most networks seem to be largely composed of occurring
patterns, called network motifs. The functions associated with common network motifs, such
as auto-regulation and feed-forward loops (FFLs), were discovered and revealed by several
researches both theoretically and experimentally [1, 9-10, 19-22].
Unsurprisingly, the function-enriched coregulation pairs also have preferentially
recurring network motifs as shown in Figure 5. Several types of motifs were found in TF-TF
coregulation; for example, bidirectional and unidirectional FFLs were explored and these
results were consistent with previous studies on network biology. In addition to FFLs, we
went further to investigate the upstream regulatory patterns of coregulation pairs and found
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that the two paired regulators were closely linked in their upstream. For instance, over half of
the pairs had common upstream TFs; a significant number of pairs had common TFs and
miRNAs; and almost all pairs were cross-regulated in their upstream. Similar results were
also found in TF-miRNA coregulation. However, no enriched motif was found in miRNAmiRNA coregulation, probably due to the incomplete knowledge of regulatory regions of
miRNA genes.
Expression patterns of coregulation pairs
Expression data across human normal/tumor tissues have recently become available. A
previous study measured miRNA and mRNA expression profiles across 8 tissues (colon,
pancreas, kidney, bladder, prostate, uterus, lung, and breast) and each tissue contained both
normal and tumor samples [18]. By analyzing the expression profiles, we investigated the
correlations between the expression profiles of each coregulation pair in both normal and
cancer samples.
Figure 6 shows the expression correlations of different coregulation types in normal and
tumor samples. Interestingly, the three coregulation types show distinct trends in normal
tissues. For example, TF-TF had a zero-centered distribution similar to background; TFmiRNA had two tendencies to highly-positive and medium-negative correlations in
comparison with its background; miRNA-miRNA showed preference for only positive
correlation. The different trends may reflect the diversity of function roles between TFs and
miRNAs; that is, TFs can act as activators (+) or repressors (-) in gene regulation; however,
miRNAs mainly act as repressors (-) by translation inhibition or transcript destabilization.
Thus, it seems that these typical trends may be mechanistically reasonable.
On the contrary, all coregulation types turn into an identical trend in tumor tissues. All
of them show similar zero-centered distributions resembled to their backgrounds. This trend
suggests that the function-enriched coregulation pairs lost their correlation in tumor tissues,
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implying the disruption of coregulation may be closely associated to cancers. Together these
results may support the functionality of identified coregulation pairs.
Discussion and Conclusion
We proposed a novel strategy aimed at identifying potential coordinated regulation by
utilizing functional annotation information and discovered many biological processes that
emerged only in coregulation. Compared to traditional function enrichment analysis, our
strategy considered whole function profiles rather than single annotations. In addition, it also
solves the restriction of traditional methods that only focus on single regulators. For example,
a miRNA can potentially regulate an abundance of target genes. To find enriched functions of
the miRNA, all its potential targets will be tested for any enriched function. However, since
the target size of a miRNA may be huge, some meaningful biological processes involving
only a small subset of genes will be hidden. In fact, these hidden processes may be
significantly impacted by miRNAs in coordination with other regulators, namely, other
miRNAs or TFs. After all, a biological process may be regulated not only at the
transcriptional layer, but also at the posttranscriptional layer [7-8, 23-24].
Interestingly, our results show that pigmentation and reproductive process are two
typical biological processes specifically emerging in TF-miRNA coregulation. It is suggested
that miRNAs may provide genetic switch mechanisms to essentially inactivate the target
genes, thus leading to detectable phenotypic consequences. In model organisms, there have
been many studies investigating the switch-like role of miRNAs in pigmentation. For
example, miRNAs can regulate the eye pigmentation genes in Drosophila [25]. The influence
of miRNAs on pigmentation in zebrafish was also reported [26]. Another study found that
miR-434-5p may mediate skin whitening and lightening in mouse [27]. And in melanoma cell
lines, it is shown that miR-137 may target a pigmentation regulator [28].
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The analysis of functional coregulation networks provided other clues. We found that a
TF may regulate in coordination with different miRNAs in different biological processes, and
vice versa. It suggested that the cross-layer coregulation may have higher specificity than
intra-layer coregulation.
We also performed network motif analysis to see if any recurring pattern exists in
coregulation network structure. Different types of feed-forward loops were found in TF-TF
and TF-miRNA coregulation, and these results were consistent with several previous studies
on transcriptional network [1, 9-10, 19-22]. Among these FFLs, a special kind of miRNAmediated FFLs emerged in TF-miRNA coregulation. In this kind of FFLs, a miRNA may
simultaneously repress a TF and its target genes, thus contributing to a switch-like control of
expression programs. More importantly, we go further this time to investigate the upstream
structure of coregulation pairs and found closely interaction in their upstream. It implies that
the network structures of coregulation may have extensive crosstalk in the higher levels.
Finally, the expression analysis of coregulation discovered distinct trends in different
coregulation types; namely, TF-TF showed no correlation, whereas miRNA-miRNA had a
preference of positive correlation, and TF-miRNA appeared both positive and negative
correlation. A previous study investigated only TF-miRNA correlation and found the same
tendencies [9]. The authors rationalized this trend by pointing out the distinct function roles
that TFs and miRNAs may play. We further supported this idea by showing the results of TFTF and miRNA-miRNA coregulation, which were also consistent with the same
interpretation. In addition, TF activities are under control at protein level; that is, TFs may be
activated or deactivated by a number of mechanisms including phosphorylation, ligand
binding, and interaction with other regulatory proteins. Therefore, it is not surprising that cofunction TFs may show no correlation in mRNA expression level. Notably, a large proportion
of TF-miRNA pairs showed negative correlation in expression profiles, which could be
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explained by the structure of the miRNA-mediated-FFLs discussed before, supporting the
idea that many miRNAs in TF-miRNA coregulation contributed to switch-like regulation.
More significantly, by comparing the expression correlations between normal and tumor
tissues, we found a common trend in function-enriched coregulation pairs; that is, the
function-enriched pairs lost their correlation in tumor tissues. It suggested that the disruption
of coregulation may lead to abnormal expression programs and may be directly associated to
cancers.
Our findings shed light on the coregulation of miRNAs in transcriptional regulatory
network. Future experimental works will provide more complete knowledge in transcriptional
network and miRNA regulation, thus allowing the elucidation of more precise co-regulatory
mechanisms.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
CYC implemented the computational method, carried out the analysis, and drafted the
manuscript. CYC and HCH conceived of the study. STC and CSF helped to perform the
analysis and participated in discussions of the research. HFJ and HCH participated in the
design and coordination of the study, and edited the manuscript. All authors read and
approved the final manuscript.
Acknowledgements
The authors wish to thank Wen-Hsiung Li and Chun-Chieh Arthur Shih for their helpful
discussions. This work was supported by National Science Council of Taiwan, National
Health Research Institutes (NHRI-EX98-9819PI), and NTU Frontier and Innovative Research
Projects.
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Figure Legends
Figure 1 - Example workflow for identifying significant coregulation
Figure 2 - Example workflow for constructing functional coregulation network
For each coregulation pair, a function linkage was established if a GO term enriched in their
shared targets. The coregulation network was generated based on these function linkages.
Nodes represent regulators, and edge represents GO terms, marked with different colours.
Figure 3 - Distributions of enriched biological processes for different regulation types
(A) Distributions for TF-involving regulation: individual TFs, TF-TF pairs, and TF-miRNA
pairs. (B) Distributions for miRNA-involving regulation: individual miRNAs, miRNAmiRNA pairs, and TF-miRNA pairs. Note that the same TF-miRNA line are drawn in both
(A) and (B) for comparison.
Figure 4 - Functional coregulation networks
(A) TF-TF coregulation network. (B) miR-miR coregulation network. (C) TF-miR
coregulation network. Red nodes represent TFs; green nodes represent miRNAs; and edges
represent biological processes. Different edge colours represent different biological
processes.
Figure 5 - Network motifs for TF-TF and TF-miRNA coregulation pairs
For each motif, the observation number and the percentage of pairs that have this motif were
reported, along with the P-value and one instance coregulation pair.
Figure 6 - Distributions of expression correlation for different types of coregulation
pairs
Left column for normal tissues; right column for tumor tissues. Rows are in the order of TFTF, TF-miR, and miR-miR coregulation pairs.
Additional files
Additional file 1 – List of all function-enriched coregulation pairs
http://idv.sinica.edu.tw/joeychen/APBC2011/AdditionalFile1.pdf
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Additional file 2 – List of network motifs for function-enriched coregulation pairs
http://idv.sinica.edu.tw/joeychen/APBC2011/AdditionalFile2.pdf
Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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